Candidate: Dr. Soyeon Kim obtained her Ph.D. degree in Statistics at Rice University. For her doctoral research, she developed statistical machine learning methods to identify biomarkers for precision medicine approaches to treating lung cancer. She is currently a post-doctoral fellow at UPMC Children?s Hospital of Pittsburgh (CHP) and supported by a T32 grant awarded to one of her mentors, Dr. Juan C. Celedn. The goal of her research is to understand the pathogenesis of diseases by developing statistical methods for integrative analysis of multi-omics data. She is an author and co-author of eight manuscripts, including ones in high-profile journals such as Nature Genetics. Environment: CHP, affiliated with the University of Pittsburgh, School of Medicine, is a leading pediatric center for clinical care, research, and educational excellence. Dr. Kim works in the Rangos Research Center, comprised of nine floors of state-of-the-art laboratories, offices, and conference facilities. In addition to superb mentoring by Drs. Wei Chen, Juan C. Celedn and George Tseng, she will use the Pittsburgh supercomputing center (PSC), an excellent source of computing powers for intensive bioinformatics work. Research: Although both single-nucleotide polymorphisms (SNPs) and DNA methylation play important roles in regulating gene expression, the associations between SNPs/DNA methylation and expression of disease genes in childhood asthma are mostly unknown. Due to the multiple-testing problem, most studies focus on identifying the regulation of gene expression by cis- (nearby) DNA features, rather than a combination of cis- and trans (far from genes) regulation. To overcome the major multiple-testing problem, we will develop a novel statistical machine learning method that can handle a large number of variables of omics data without multiple- testing correction issues. In Aim 1, I will identify cis and trans genetic (SNPs) and epigenetic (methylation) factors that regulate gene expression in the nasal epithelium of asthmatic and healthy subjects. Through this analysis, I will uncover novel SNPs and methylation CpGs that may have not been found in genome-wide association studies (GWAS) or epigenome-wide association studies (EWAS). In Aim 2, I will estimate the genetic and epigenetic contributions to the expressions of genes that are associated with atopic asthma. This study also will classify genes that are affected the most by genetic factors and genes that are affected mostly by epigenetic factors, which include environmental factors. In Aim 3, I will develop statistical methods to identify SNPs that indirectly regulate gene expression through methylation in nasal epithelium of asthmatic and healthy subjects. The outcome of this work will be a better understanding of the abnormal molecular and cellular mechanisms through which asthma develops, and the application of this knowledge to help to produce new therapies for asthma.